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library_name: transformers
license: apache-2.0
datasets:
- zerofata/Roleplay-Anime-Characters
- zerofata/Instruct-Anime-CreativeWriting
- zerofata/Summaries-Anime-FandomPages
base_model:
- mistralai/Mistral-Small-3.2-24B-Instruct-2506
---
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</style>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Painted Fantasy</title>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
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</head>
<body>
<div class="container">
<div class="title-container">
<!-- Glitchy overlay -->
<div class="glitchy-overlay"></div>
<!-- Main title -->
<div class="title-wrapper">
<h1 class="title-main">
PAINTED <span class="lemonade-text">FANTASY</span> VISAGE
</h1>
<div class="title-subtitle">
<span class="subtitle-text">Mistrall Small 3.2 Upscaled 33B</span>
</div>
</div>
</div>
<div class="section-container">
<div class="section-header">
<div class="section-indicator"></div>
<h2 class="section-title">Overview</h2>
</div>
<div class="section-content">
<p>Another experimental release. Mistral Small 3.2 24B upscaled by 18 layers to create a 33.6B model. This model then went through pretraining, SFT & DPO.</p>
<p>Can't guarantee the Mistral 3.2 repetition issues are fixed, but this model seems to be less repetitive than my previous attempt.</p>
<p>This is an uncensored creative model intended to excel at character driven RP / ERP where characters are portrayed creatively and proactively.</p>
</div>
</div>
<div class="section-container">
<div class="section-header">
<div class="section-indicator"></div>
<h2 class="section-title">SillyTavern Settings</h2>
</div>
<div class="section-content">
<h3 class="subheading">Recommended Roleplay Format</h3>
<div class="data-box">
<div class="data-row">
<span class="data-arrow">></span>
<span class="data-label">Actions:</span>
<span>In plaintext</span>
</div>
<div class="data-row">
<span class="data-arrow">></span>
<span class="data-label">Dialogue:</span>
<span>"In quotes"</span>
</div>
<div class="data-row">
<span class="data-arrow">></span>
<span class="data-label">Thoughts:</span>
<span>*In asterisks*</span>
</div>
</div>
<h3 class="subheading">Recommended Samplers</h3>
<div class="data-box">
<div class="data-row">
<span class="data-arrow">></span>
<span class="data-label">Temp:</span>
<span>0.6</span>
</div>
<div class="data-row">
<span class="data-arrow">></span>
<span class="data-label">MinP:</span>
<span>0.03 - 0.05</span>
</div>
<div class="data-row">
<span class="data-arrow">></span>
<span class="data-label">TopP:</span>
<span>0.95 - 1.0</span>
</div>
<div class="data-row">
<span class="data-arrow">></span>
<span class="data-label">Dry:</span>
<span>0.8, 1.75, 4</span>
</div>
</div>
<h3 class="subheading">Instruct</h3>
<div class="data-box">
<p style="margin: 0;">Mistral v7 Tekken</p>
</div>
</div>
</div>
<div class="section-container">
<div class="section-header">
<div class="section-indicator"></div>
<h2 class="section-title">Quantizations</h2>
</div>
<div class="section-content">
<div style="margin-bottom: 20px;">
<h3 class="subheading">GGUF</h3>
<div class="data-box">
<div class="data-row">
<a href="https://huggingface.co/mradermacher/MS3.2-PaintedFantasy-Visage-33B-GGUF">Static (mradermacher)</a>
</div>
<div class="data-row">
<a href="https://huggingface.co/mradermacher/MS3.2-PaintedFantasy-Visage-33B-i1-GGUF">iMatrix (mradermacher)</a>
</div>
</div>
</div>
<div>
<h3 class="subheading">EXL3</h3>
<div class="data-box">
<div class="data-row">
<a href="https://huggingface.co/zerofata/MS3.2-PaintedFantasy-Visage-33B_exl3_3bpw">3bpw</a>
</div>
<div class="data-row">
<a href="https://huggingface.co/zerofata/MS3.2-PaintedFantasy-Visage-33B_exl3_4bpw">4bpw</a>
</div>
<div class="data-row">
<a href="https://huggingface.co/zerofata/MS3.2-PaintedFantasy-Visage-33B_exl3_5bpw">5bpw</a>
</div>
<div class="data-row">
<a href="https://huggingface.co/zerofata/MS3.2-PaintedFantasy-Visage-33B_exl3_6bpw">6bpw</a>
</div>
</div>
</div>
</div>
</div>
<div class="section-container">
<div class="section-header">
<div class="section-indicator"></div>
<h2 class="section-title">Creation Process</h2>
</div>
<div class="section-content">
<p>Creation process: Upscale > Pretrain > SFT > DPO</p>
<p>All training was qlora (including pretrain).</p>
<p>Pretrained on 177MB of data. Dataset consisteted mostly of Light Novels, NSFW stories, SFW stories and filled out with general corpus text from Huggingface FineWeb-2 dataset.</p>
<p>The model then went through SFT using a dataset of approx 3.6 million tokens, 700 RP conversations, 1000 creative writing / instruct samples and about 100 summaries. The bulk of this data has been made public.</p>
<p>Finally, DPO was used to make the model more consistent.</p>
<div class="dropdown-container">
<details>
<summary class="dropdown-summary">
<span class="dropdown-arrow">></span>
Mergekit Config
</summary>
<div class="dropdown-content">
<pre><code>base_model: anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-Text-Only
merge_method: passthrough
dtype: bfloat16
slices:
- sources:
- model: anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-Text-Only
layer_range: [0, 29]
- sources:
- model: anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-Text-Only
layer_range: [10, 39]
</code></pre>
</div>
</details>
</div>
<div class="dropdown-container">
<details>
<summary class="dropdown-summary">
<span class="dropdown-arrow">></span>
Axolotl configs
</summary>
<div class="dropdown-content">
<p>Not optimized for cost / performance efficiency, YMMV.</p>
<div class="config-title">SFT 1*H100</div>
<pre><code># ====================
# MODEL CONFIGURATION
# ====================
base_model: ./Upscale_Mistral-PT/merged
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
chat_template: mistral_v7_tekken
<br>
# ====================
# DATASET CONFIGURATION
# ====================
datasets:
- path: ./dataset.jsonl
type: chat_template
split: train
chat_template_strategy: tokenizer
field_messages: messages
message_property_mappings:
role: role
content: content
roles:
user: ["user"]
assistant: ["assistant"]
system: ["system"]
dataset_prepared_path:
train_on_inputs: false # Only train on assistant responses
# ====================
# QLORA CONFIGURATION
# ====================
adapter: qlora
load_in_4bit: true
lora_r: 128
lora_alpha: 128
lora_dropout: 0.1
lora_target_linear: true
# lora_modules_to_save: # Uncomment only if you added NEW tokens
# ====================
# TRAINING PARAMETERS
# ====================
num_epochs: 2
micro_batch_size: 4
gradient_accumulation_steps: 2
learning_rate: 1.5e-5
optimizer: paged_adamw_8bit
lr_scheduler: rex
warmup_ratio: 0.05
weight_decay: 0.01
max_grad_norm: 1.0
# ====================
# SEQUENCE & PACKING
# ====================
sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
# ====================
# HARDWARE OPTIMIZATIONS
# ====================
bf16: auto
flash_attention: true
gradient_checkpointing: true
# ====================
# EVALUATION & CHECKPOINTING
# ====================
save_strategy: steps
save_steps: 5
save_total_limit: 5 # Keep best + last few checkpoints
load_best_model_at_end: true
greater_is_better: false
# ====================
# LOGGING & OUTPUT
# ====================
output_dir: ./Upscale_Mistral-PT-SFT-2
logging_steps: 2
save_safetensors: true
# ====================
# WANDB TRACKING
# ====================
wandb_project: MS3-2-SFT
wandb_entity: your_entity
wandb_name: run_name<p></p></code></pre>
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